[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100648869":3},{"organization":4,"armGroups":7,"interventions":10,"overallOfficials":10,"centralContacts":12,"locations":18,"responsibleParty":91,"collaborators":10,"id":94,"slug":95,"hasResults":96,"nctId":97,"briefTitle":98,"officialTitle":99,"acronym":10,"eligibilityCriteria":100,"healthyVolunteers":96,"sex":101,"minAge":102,"maxAge":10,"enrollmentInfo":103,"targetDuration":10,"studyType":106,"phases":10,"briefSummary":107,"conditions":108,"keywords":110,"overallStatus":115,"whyStopped":10,"lastUpdateSubmitDate":116,"lastUpdatePostDateStruct":117,"startDateStruct":120,"completionDateStruct":122,"leadSponsor":124,"locationsCount":125},{"fullName":5,"class":6},"Guangdong Provincial People's Hospital","OTHER",[8],{"label":9,"type":10,"description":11,"interventionNames":10},"Single group",null,"This study has only one group.",[13],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},"Yijing Feng, PhD","CONTACT","8613650882360","yfeng@g.harvard.edu",[19,37,51,61,76],{"facility":20,"status":10,"city":21,"state":22,"zip":23,"country":24,"countryCode":25,"cosmosGeoPoint":26,"geoPoint":31,"contacts":32},"Second Affiliated Hospital of Army Medical University (Xinqiao Hospital)","Chongqing","Chongqing Municipality","400037","China","CN",{"type":27,"coordinates":28},"Point",[29,30],106.55771,29.56026,{"lat":30,"lon":29},[33],{"name":34,"role":15,"phone":35,"phoneExt":10,"email":36},"Jigang Dai","86 23 6875 5114","cqdaijigangzhushou@163.com",{"facility":5,"status":10,"city":38,"state":39,"zip":40,"country":24,"countryCode":25,"cosmosGeoPoint":41,"geoPoint":45,"contacts":46},"Guangzhou","Guangdong","510000",{"type":27,"coordinates":42},[43,44],113.25,23.11667,{"lat":44,"lon":43},[47,48],{"name":14,"role":15,"phone":16,"phoneExt":10,"email":17},{"name":49,"role":50,"phone":10,"phoneExt":10,"email":10},"Wenzhao Zhong, PhD","PRINCIPAL_INVESTIGATOR",{"facility":52,"status":10,"city":38,"state":39,"zip":40,"country":24,"countryCode":25,"cosmosGeoPoint":53,"geoPoint":55,"contacts":56},"Zhujiang Hospital, Southern Medical University",{"type":27,"coordinates":54},[43,44],{"lat":44,"lon":43},[57],{"name":58,"role":15,"phone":59,"phoneExt":10,"email":60},"Guibin Qiao","86 20 61643888","qiaoguibin@smu.edu.cn",{"facility":62,"status":10,"city":63,"state":64,"zip":65,"country":24,"countryCode":25,"cosmosGeoPoint":66,"geoPoint":70,"contacts":71},"Affiliated Hospital of Xuzhou Medical University","Xuzhou","Jiangsu","221006",{"type":27,"coordinates":67},[68,69],117.28386,34.20442,{"lat":69,"lon":68},[72],{"name":73,"role":15,"phone":74,"phoneExt":10,"email":75},"Hao Zhang","0516 8560 9999","haozhang_xz@163.com",{"facility":77,"status":10,"city":78,"state":79,"zip":80,"country":24,"countryCode":25,"cosmosGeoPoint":81,"geoPoint":85,"contacts":86},"Zhejiang University","Hangzhou","Zhejiang","310003",{"type":27,"coordinates":82},[83,84],120.16142,30.29365,{"lat":84,"lon":83},[87],{"name":88,"role":15,"phone":89,"phoneExt":10,"email":90},"Junqiang Fan","86 57187951111","zrxwk@zju.edu.cn",{"type":50,"investigatorFullName":92,"investigatorTitle":93,"investigatorAffiliation":5,"oldNameTitle":10,"oldOrganization":10},"Wen-zhao ZHONG","Vice President, Guangdong Provincial People's Hospital","100648869","multicenter-prospective-validation-of-ai-models-for-malignancy-risk-prediction-in-pulmonary-nodules-100648869",false,"NCT07727122","Multicenter Prospective Validation of AI Models for Malignancy Risk Prediction in Pulmonary Nodules","A Multicenter Prospective Diagnostic Accuracy Study of Three CT-Based Artificial Intelligence Models for Predicting Malignancy Risk in Pulmonary Nodules Using Pathology as the Gold Standard","Inclusion Criteria:\n\n* Age ≥ 18 years, any sex.\n* At least one pulmonary nodule detected on chest CT, with initial nodule diameter ≤ 3 cm.\n* The nodule undergoes surgical resection or biopsy with a definitive benign or malignant pathological diagnosis.\n* Time interval between CT examination and pathological examination ≤ 6 months.\n* Availability of complete CT imaging data in DICOM format with adequate image quality (no severe artifacts), meeting input requirements of all three AI models.\n\nAvailability of complete clinicopathologic information including histologic type and grade, with clear pathological diagnosis suitable as gold standard labels for AI validation.\n\n-The patient (or legally authorized representative) is willing and able to sign written informed consent.\n\nExclusion Criteria:\n\n* Pathological results are unclear, inconclusive, or disputed; nodule nature or grade cannot be reliably determined.\n* The patient receives treatments between CT and pathology that may significantly alter nodule appearance (e.g., chemotherapy, radiotherapy, targeted therapy).\n* CT imaging data are incomplete (missing essential series) or have severe motion, metal, or other artifacts preventing accurate AI analysis.\n* Required metadata for any AI model are missing and cannot be imputed. History of other malignant tumors (malignancies other than the index non-small cell lung cancer).\n* Severe psychiatric illness, cognitive impairment, or other conditions that prevent cooperation with study-related procedures and follow-up.\n\nParticipation in another clinical study that may interfere with the results of this research.\n\n-The patient or legal representative refuses participation.\n\nExclusion (Post-Enrollment \u002F Removal from Analysis)\n\nParticipants already enrolled may be excluded from the analysis set if:\n\n* They are later found not to meet inclusion criteria or to meet exclusion criteria.\n* No usable data are available after enrollment.\n* Required AI model assessments are not completed (e.g., technical failure to generate outputs).\n* Critical data are missing, preventing contribution to primary analysis.\n* The interval between CT and pathology exceeds 6 months.","ALL","18 Years",{"count":104,"type":105},3000,"ESTIMATED","OBSERVATIONAL","This multicenter prospective diagnostic accuracy study will compare the performance of three artificial intelligence (AI) models (MVCS, LungDoc, and a United Imaging AI model) for predicting the malignancy risk of pulmonary nodules on chest CT. All enrolled patients will have pulmonary nodules ≤3 cm on CT and a definitive postoperative or biopsy pathological diagnosis. The AI models will generate continuous malignancy probability scores based only on CT images. Pathology will serve as the gold standard.\n\nThe primary objective is to compare the area under the receiver operating characteristic curve (AUC) for malignancy prediction among the three AI models. Secondary objectives include comparison of sensitivity, specificity, positive and negative predictive values, accuracy, F1 score, and calibration. Exploratory analyses will evaluate the MVCS model for predicting pathological invasion degree (pre-invasive, minimally invasive, and invasive adenocarcinoma) and an extended MVCSN model that incorporates clinical and imaging features in a data-complete subset.",[109],"Pulmonary Nodules",[111,112,113,114],"Pulmonary nodule","Artificial intelligence","Deep learning","Diagnostic accuracy","NOT_YET_RECRUITING","2026-07-22",{"date":118,"type":119},"2026-07-27","ACTUAL",{"date":121,"type":105},"2026-07",{"date":123,"type":105},"2028-12",{"name":5,"class":6},5]